11. DeconvNet
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Encoder で max pooling した位置を記録
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Decoder の unpooling 時に使用
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DeconvNet 単体での精度は良好とは言えず、
FCN とのアンサンブルで効果を主張。曰く、
「 DeconvNet は輪郭を捉え、
FCN は概形を捉えることに長けている」
”our deconvolution network is appropriate to capture the fine-details of an object,
whereas FCN is typically good at extracting the overall shape of an object.”
34. 出典
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[1] Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation."
Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
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[2] Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image
segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham,
2015.
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[3] Noh, Hyeonwoo, Seunghoon Hong, and Bohyung Han. "Learning deconvolution network for semantic segmentation."
Proceedings of the IEEE international conference on computer vision. 2015.
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[4] Badrinarayanan, Vijay, Alex Kendall, and Roberto Cipolla. "Segnet: A deep convolutional encoder-decoder architecture
for image segmentation." IEEE transactions on pattern analysis and machine intelligence 39.12 (2017): 2481-2495.
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[5] Li, Ruirui, et al. "DeepUNet: a deep fully convolutional network for pixel-level sea-land segmentation." IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensing 99 (2018): 1-9.
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[6] Oktay, Ozan, et al. "Attention U-Net: learning where to look for the pancreas." arXiv preprint arXiv:1804.03999 (2018).
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[7] Zhao, Hengshuang, et al. "Pyramid scene parsing network." Proceedings of the IEEE conference on computer vision and
pattern recognition. 2017.
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[8] Chen, Liang-Chieh, et al. "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and
fully connected crfs." IEEE transactions on pattern analysis and machine intelligence 40.4 (2018): 834-848.
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[9] Chen, Liang-Chieh, et al. "Encoder-decoder with atrous separable convolution for semantic image segmentation."
Proceedings of the European Conference on Computer Vision (ECCV). 2018.
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[10] Mehta, Sachin, et al. "Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation." Proceedings
of the European Conference on Computer Vision (ECCV). 2018.
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[11] Alom, Md Zahangir, et al. "Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image
segmentation." arXiv preprint arXiv:1802.06955 (2018).
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[12] Liu, Chenxi, et al. "Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation." arXiv
preprint arXiv:1901.02985 (2019).